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add continual retraining feature, handly mypy typing reqs, improve docstrings

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This commit is contained in:
robcaulk
2022-08-24 12:54:02 +02:00
parent b708134c1a
commit c0cee5df07
11 changed files with 387 additions and 362 deletions
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4
freqtrade/freqai/prediction_models/BaseClassifierModel.py
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@@ -57,9 +57,9 @@ class BaseClassifierModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
f' features and {len(data_dictionary["train_features"])} data points'
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary)

4
freqtrade/freqai/prediction_models/BaseRegressionModel.py
View File

@@ -56,9 +56,9 @@ class BaseRegressionModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
f' features and {len(data_dictionary["train_features"])} data points'
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary)

4
freqtrade/freqai/prediction_models/BaseTensorFlowModel.py
View File

@@ -53,9 +53,9 @@ class BaseTensorFlowModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
f'Training model on {len(dk.data_dictionary["train_features"].columns)}'
f' features and {len(data_dictionary["train_features"])} data points'
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
model = self.fit(data_dictionary)

19
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@@ -1,7 +1,6 @@
import logging
from typing import Any, Dict # , Tuple
from typing import Any, Dict
# import numpy.typing as npt
import torch as th
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
@@ -22,12 +21,18 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[256, 256, 128])
net_arch=[512, 512, 256])
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=Path(dk.data_path / "tensorboard"),
**self.freqai_info['model_training_parameters']
)
if dk.pair not in self.dd.model_dictionary or not self.continual_retraining:
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=Path(dk.data_path / "tensorboard"),
**self.freqai_info['model_training_parameters']
)
else:
logger.info('Continual training activated - starting training from previously '
'trained agent.')
model = self.dd.model_dictionary[dk.pair]
model.set_env(self.train_env)
model.learn(
total_timesteps=int(total_timesteps),

255
freqtrade/freqai/prediction_models/ReinforcementLearnerCustomAgent.py
View File

@@ -1,255 +0,0 @@
import logging
import torch as th
from typing import Any, Dict, List, Optional, Tuple, Type, Union
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from stable_baselines3 import DQN
from stable_baselines3.common.buffers import ReplayBuffer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from pathlib import Path
from stable_baselines3.dqn.policies import (CnnPolicy, DQNPolicy, MlpPolicy,
QNetwork)
from torch import nn
import gym
from stable_baselines3.common.torch_layers import (BaseFeaturesExtractor,
FlattenExtractor)
from stable_baselines3.common.type_aliases import GymEnv, Schedule
from stable_baselines3.common.policies import BasePolicy
logger = logging.getLogger(__name__)
class ReinforcementLearnerCustomAgent(BaseReinforcementLearningModel):
"""
User can customize agent by defining the class and using it directly.
Here the example is "TDQN"
"""
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
train_df = data_dictionary["train_features"]
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[256, 256, 128])
# TDQN is a custom agent defined below
model = TDQN(self.policy_type, self.train_env,
tensorboard_log=Path(dk.data_path / "tensorboard"),
policy_kwargs=policy_kwargs,
**self.freqai_info['model_training_parameters']
)
model.learn(
total_timesteps=int(total_timesteps),
callback=self.eval_callback
)
if Path(dk.data_path / "best_model.zip").is_file():
logger.info('Callback found a best model.')
best_model = self.MODELCLASS.load(dk.data_path / "best_model")
return best_model
logger.info('Couldnt find best model, using final model instead.')
return model
# User creates their custom agent and networks as shown below
def create_mlp_(
input_dim: int,
output_dim: int,
net_arch: List[int],
activation_fn: Type[nn.Module] = nn.ReLU,
squash_output: bool = False,
) -> List[nn.Module]:
dropout = 0.2
if len(net_arch) > 0:
number_of_neural = net_arch[0]
modules = [
nn.Linear(input_dim, number_of_neural),
nn.BatchNorm1d(number_of_neural),
nn.LeakyReLU(),
nn.Dropout(dropout),
nn.Linear(number_of_neural, number_of_neural),
nn.BatchNorm1d(number_of_neural),
nn.LeakyReLU(),
nn.Dropout(dropout),
nn.Linear(number_of_neural, number_of_neural),
nn.BatchNorm1d(number_of_neural),
nn.LeakyReLU(),
nn.Dropout(dropout),
nn.Linear(number_of_neural, number_of_neural),
nn.BatchNorm1d(number_of_neural),
nn.LeakyReLU(),
nn.Dropout(dropout),
nn.Linear(number_of_neural, output_dim)
]
return modules
class TDQNetwork(QNetwork):
def __init__(self,
observation_space: gym.spaces.Space,
action_space: gym.spaces.Space,
features_extractor: nn.Module,
features_dim: int,
net_arch: Optional[List[int]] = None,
activation_fn: Type[nn.Module] = nn.ReLU,
normalize_images: bool = True
):
super().__init__(
observation_space=observation_space,
action_space=action_space,
features_extractor=features_extractor,
features_dim=features_dim,
net_arch=net_arch,
activation_fn=activation_fn,
normalize_images=normalize_images
)
action_dim = self.action_space.n
q_net = create_mlp_(self.features_dim, action_dim, self.net_arch, self.activation_fn)
self.q_net = nn.Sequential(*q_net).apply(self.init_weights)
def init_weights(self, m):
if type(m) == nn.Linear:
th.nn.init.kaiming_uniform_(m.weight)
class TDQNPolicy(DQNPolicy):
def __init__(
self,
observation_space: gym.spaces.Space,
action_space: gym.spaces.Space,
lr_schedule: Schedule,
net_arch: Optional[List[int]] = None,
activation_fn: Type[nn.Module] = nn.ReLU,
features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
normalize_images: bool = True,
optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
optimizer_kwargs: Optional[Dict[str, Any]] = None,
):
super().__init__(
observation_space=observation_space,
action_space=action_space,
lr_schedule=lr_schedule,
net_arch=net_arch,
activation_fn=activation_fn,
features_extractor_class=features_extractor_class,
features_extractor_kwargs=features_extractor_kwargs,
normalize_images=normalize_images,
optimizer_class=optimizer_class,
optimizer_kwargs=optimizer_kwargs
)
@staticmethod
def init_weights(module: nn.Module, gain: float = 1) -> None:
"""
Orthogonal initialization (used in PPO and A2C)
"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
nn.init.kaiming_uniform_(module.weight)
if module.bias is not None:
module.bias.data.fill_(0.0)
def make_q_net(self) -> TDQNetwork:
# Make sure we always have separate networks for features extractors etc
net_args = self._update_features_extractor(self.net_args, features_extractor=None)
return TDQNetwork(**net_args).to(self.device)
class TMultiInputPolicy(TDQNPolicy):
def __init__(
self,
observation_space: gym.spaces.Space,
action_space: gym.spaces.Space,
lr_schedule: Schedule,
net_arch: Optional[List[int]] = None,
activation_fn: Type[nn.Module] = nn.ReLU,
features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
normalize_images: bool = True,
optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
optimizer_kwargs: Optional[Dict[str, Any]] = None,
):
super().__init__(
observation_space,
action_space,
lr_schedule,
net_arch,
activation_fn,
features_extractor_class,
features_extractor_kwargs,
normalize_images,
optimizer_class,
optimizer_kwargs,
)
class TDQN(DQN):
policy_aliases: Dict[str, Type[BasePolicy]] = {
"MlpPolicy": MlpPolicy,
"CnnPolicy": CnnPolicy,
"TMultiInputPolicy": TMultiInputPolicy,
}
def __init__(
self,
policy: Union[str, Type[TDQNPolicy]],
env: Union[GymEnv, str],
learning_rate: Union[float, Schedule] = 1e-4,
buffer_size: int = 1000000, # 1e6
learning_starts: int = 50000,
batch_size: int = 32,
tau: float = 1.0,
gamma: float = 0.99,
train_freq: Union[int, Tuple[int, str]] = 4,
gradient_steps: int = 1,
replay_buffer_class: Optional[ReplayBuffer] = None,
replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
optimize_memory_usage: bool = False,
target_update_interval: int = 10000,
exploration_fraction: float = 0.1,
exploration_initial_eps: float = 1.0,
exploration_final_eps: float = 0.05,
max_grad_norm: float = 10,
tensorboard_log: Optional[Path] = None,
create_eval_env: bool = False,
policy_kwargs: Optional[Dict[str, Any]] = None,
verbose: int = 1,
seed: Optional[int] = None,
device: Union[th.device, str] = "auto",
_init_setup_model: bool = True,
):
super().__init__(
policy=policy,
env=env,
learning_rate=learning_rate,
buffer_size=buffer_size,
learning_starts=learning_starts,
batch_size=batch_size,
tau=tau,
gamma=gamma,
train_freq=train_freq,
gradient_steps=gradient_steps,
replay_buffer_class=replay_buffer_class, # No action noise
replay_buffer_kwargs=replay_buffer_kwargs,
optimize_memory_usage=optimize_memory_usage,
target_update_interval=target_update_interval,
exploration_fraction=exploration_fraction,
exploration_initial_eps=exploration_initial_eps,
exploration_final_eps=exploration_final_eps,
max_grad_norm=max_grad_norm,
tensorboard_log=tensorboard_log,
create_eval_env=create_eval_env,
policy_kwargs=policy_kwargs,
verbose=verbose,
seed=seed,
device=device,
_init_setup_model=_init_setup_model
)